1. Field
The present application relates generally to methods and systems to assess a severity of vessel obstruction, and methods and systems to form feature-perfusion classification (FPC) models that classify training features in connection with assessing a severity of vessel obstruction.
2. State of the Art
Coronary artery disease (CAD) is one of the leading causes of death worldwide. CAD generally refers to conditions that involve narrowed or blocked blood vessels that can lead to reduced or absent blood supply to the sections distal to the stenosis resulting in reduced oxygen supply to the myocardium, resulting in, for instance, ischemia and chest pain (angina). A very important aspect in the prevention and treatment of CAD is the functional assessment of such narrowed or blocked blood vessels.
Presently, X-ray angiography is the imaging modality used during treatment of stenotic (narrowed) coronary arteries by means of a minimally invasive procedure also known as percutaneous coronary intervention (PCI). During PCI, a (interventional) cardiologist feeds a deflated balloon or other device on a catheter from the inguinal femoral artery or radial artery up through blood vessels until they reach the site of blockage in the artery. X-ray imaging is used to guide the catheter threading. PCI usually involves inflating a balloon to open the artery with the aim to restore unimpeded blood flow. Stents or scaffolds may be placed at the site of the blockage to hold the artery open.
X-ray angiography is also a standard imaging technique for anatomical assessment of the coronary arteries and the diagnosis of coronary artery disease. Although objectivity, reproducibility and accuracy in assessment of lesion severity has improved by means of quantitative coronary analysis tools (QCA), the physiological significance of atherosclerotic lesions, which is the most important prognostic factor in patients with coronary artery disease, cannot be appreciated by X-ray angiography.
For intermediate coronary lesions (defined as luminal narrowing of 30-70%), for instance, it is not always obvious if the stenosis is a risk for the patient and if it is desired to take action. Overestimation of the severity of the stenosis can cause a treatment which in hindsight would not have been necessary and therefore exposing the patient to risks that are not necessary. Underestimation of the severity of the stenosis, however, could induce risks because the patient is left untreated while the stenosis is in reality severe and actually impedes flow to the myocardium. Especially for these situations it is desired to have an additional functional assessment to aid in a good decision making.
Fractional Flow Reserve (FFR) has been used increasingly over the last 10-15 years as a method to identify and effectively target the coronary lesion most likely to benefit from percutaneous coronary intervention (PCI). FFR is a technique used to measure pressure differences across a coronary artery stenosis to determine the likelihood that the stenosis impedes oxygen delivery to the heart muscle. The technique involves percutaneously inserting a pressure-transducing wire inside the coronary artery and measuring the pressure behind (distal to) and before (proximal to) the lesion. This is best done in a hyperemic state because in the case of maximum hyperemia, blood flow to the myocardium is proportional to the myocardium perfusion pressure. FFR therefore provides a quantitative assessment of the functional severity of the coronary lesion as described in Pijls et al., “Measurement of Fractional Flow Reserve to Assess the Functional Severity of Coronary Artery Stenoses,” N Engl J Med 1996, 334:1703-1708.
Although the European Society of Cardiology (ESC) and the American College of Cardiology/American Heart Association (ACC/AHA) guidelines recommend the use of FFR in patients with intermediate coronary stenosis (30-70%), visual assessment, whether or not supported by QCA, of X-ray coronary angiograms alone is still used in over 90% of procedures to select patients for percutaneous coronary intervention (Kleiman et al, “Bringing it all together: integration of physiology with anatomy during cardiac catheterization,” J Am Coll Cardiol. 2011; 58:1219-1221).
FFR, however, has some disadvantages. The technique is associated with the additional cost of a pressure wire which can only be used once. Furthermore, measuring FFR requires invasive catheterization with the associated cost and procedure time. Also, in order to induce (maximum) hyperemia, additional drug infusion (adenosine or papaverine) is required, which is an extra burden for the patient.
Coronary computed tomography (CT) angiography (CCTA) is a non-invasive imaging modality for the anatomic assessment of coronary arteries but does not assess the functional significance of coronary lesions. Due to the remarkably high negative predictive value of CCTA and its non-invasive nature, the main strength of CCTA is its excellent ability to exclude CAD. Although CCTA can reliably exclude the presence of significant coronary artery disease, many high-grade stenosis seen on CCTA are not flow limiting. This potential for false positive results has raised concerns that widespread use of CCTA may lead to clinically unnecessary coronary revascularization procedures. This lack of specificity of CCTA is one of the main limitations of CCTA in determining the hemodynamic significance of CAD (Meijboom et al, “Comprehensive assessment of coronary artery stenoses: computed tomography coronary angiography versus conventional coronary angiography and correlation with fractiona low reserve in patients with stable angina,” Journal of the American College of Cardiology 52 (8) (2008) 636-643). As a result, CCTA may lead to unnecessary interventions on the patient, which may pose added risks to patients and may result in unnecessary health care costs.
Taylor et al “Computational Fluid Dynamics Applied to Cardiac Computed Tomography for Noninvasive Quantification of Fractional Flow Reserve,” Journal of the American College of Cardiology, Vol. 61, No. 22, 2013, and U.S. Pat. No. 8,315,812, describe a noninvasive method for quantifying FFR from CCTA, which we refer to as FFRCT. This technology uses computational fluid dynamics (CFD) applied to CCTA after semi-automated segmentation of the coronary tree including a part of the ascending aorta covering the region in which both the left coronary artery as well as the right coronary artery emanate. Three-dimensional (3D) blood flow and pressure of the coronary arteries are simulated, with blood modeled as an incompressible Newtonian fluid with Navier-Stokes equations and solved subject to appropriate initial and boundary conditions with a finite element method on parallel supercomputer. The FFRCT is modeled for conditions of adenosine-induced hyperemia without adenosine infusion. This process is computationally complex and time-consuming and may require several hours.
There are several limitations for physiologic assessment of coronary stenosis by FFRCT besides its long computation time as mentioned above.
First, FFRCT is calculated by computational simulation of adenosine mediated hyperemia rather than by actual administration of adenosine.
Second, the value of FFRCT is influenced not only by stenosis severity but also by the presence of viable or scarred myocardium (De Caterina et al, “Limitations of noninvasive measurement of fractional flow reserve from coronary computed tomography angiography,” Journal of the American College of Cardiology, vol. 59, no. 15, pp. 1408-1410, 2012). The status of myocardial microvasculature indicates if a certain portion of the heart can be regarded to be healthy. For instance, the presence of myocardial ischemia is an indication that a certain portion of the heart is not supplied with enough blood for example due to an (earlier) infarction (FIG. 1). This has an effect on the microvascular resistance and should be adjusted accordingly in the model calculations.
Third, the calculated FFRCT values may be lower than compared to FFR values measured invasively in patients with microvascular disease, because modeling of adenosine-induced hyperemia may overestimate the degree of vasodilation (Taylor et al, “Computational fluid dynamics applied to cardiac computed tomography for noninvasive quantification of fractional flow reserve: scientific basis,” Journal of the American College of Cardiology, vol. 61, no. 22, pp. 2233-2241, 2013).
Fourth, vascular remodeling and collateral flow are not considered and even not visible on CCTA, therefore the assumption is made that no collateral arteries are present which feed the coronary vessel bed distal to the lesion. Collateral flow is an adaptation of the vessels where the collateral vessels provide the myocardium with blood by bypassing the stenotic lesion (FIG. 2). The effect of this is that, even in the case of a very severe stenosis (for instance a total occlusion) the sections distal to the stenosis receive blood flow. Therefore, in practice the effect of the stenosis is not necessarily severe, and a revascularization is not always needed. When collateral flow is present, this also has an effect on the calculations and should also be compensated. However, due to their small size these collateral vessels are not commonly visible on CCTA and further steps are needed to determine the presence of collateral flow.
Fifth, because FFRCT requires accurate anatomic models, numerous artifacts on CCTA may affect FFRCT calculation, such as blooming artefacts caused by large arterial calcifications and stents. In addition, motion, lower SNR, and mis-registration may compromise its accuracy. Therefore, CCTA data with good image quality is essential for the accuracy of FFRCT interpretation.
In order to keep the computational demands on a feasible level a reduced model can be used in the calculation. Specifically, sections of the coronary tree can be represented by a one-dimensional network or zero-dimensional (lumped) model. This multi-scale approach was adopted by Kim et al, “Patient-specific modeling of blood flow and pressure in human coronary arteries,” Annals of Biomedical Engineering 38, 3195-3209, 2010 to compute physiologically realistic pressure and flow waveforms in coronary vessels at baseline conditions.
Nickisch et al. “Learning patient-specific lumped models for interactive coronary blood flow simulations”, International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, 2015, pp. 433-441, presents a technique to estimate FFR in the coronary artery tree from a CCTA scan, based on blood flow simulations using a parametric patient specific lumped model. This technique is designed to further reduce computational demands. In the aforementioned publication the authors use a hydraulic system analogy to model the coronary tree with an electrical circuit interpretation where volumetric flow rate was modeled as an electrical current and pressure in the coronary artery as a voltage. This technique achieved high accuracy and real-time feedback, but it strongly depends on the segmentation of the coronary artery tree and determination of its centerline. Moreover, the method requires further clinical validation as it was only validated on a small set of CCTA scans.
A different approach to reduce the computation time required by CFD, is introduced in WO2015/058044. In this work, a method is disclosed to assess the FFR by means of a machine learning system which is based on features extracted from the anatomical three-dimensional coronary geometry. The machine-learning is trained by using geometric extracted features from synthetically generated 3D stenosis geometries and FFR values corresponding to the synthetically generated 3D stenosis computed by use of CFD. After the learning phase, the system predicts the FFR based on extraction of the same features of an unseen anatomical three-dimensional coronary geometry which is for instance extracted from CCTA by means of image segmentation methods.
A similar approach is disclosed in US2014/0073977 for assessment of FFR by means of machine-learning algorithm on geometrical features extracted from three-dimensional vessel geometry. In this method the machine learning was performed by extracted 3D coronary tree geometries from patient image data and FFR values corresponding to the patient geometries were computed by CFD.
All of the above described methods heavily rely on the anatomical vessel geometry extracted from the patient's image data. This involves, for assessment of FFR in coronaries, the segmentation of coronary tree. The demands on the segmentation accuracy are high, especially for stenotic segments. Taking into account that a mild coronary obstruction has an average diameter between 1.5-2.5 mm and that spatial resolution of CCTA is in the range of 0.25 mm isotropic, obtaining accurate 3D morphology by means of segmentation is a very challenging task. This in addition to the imaging artifacts induced by calcified coronary atherosclerotic lesions, or other imaging artifacts as discussed before.
Methods to assess the functional significance of a coronary lesion that do not rely on the anatomical coronary vessel geometry combined with blood flow modeling have been developed. For example, George et al. in “Adenosine stress 64- and 256-row detector computed tomography angiography and perfusion imaging a pilot study evaluating the transmural extent of perfusion abnormalities to predict atherosclerosis causing myocardial ischemia,” Circulation: Cardiovascular Imaging 2 (3) (2009) 174-182, demonstrated that comparison of myocardial regions imaged at rest and pharmacologically-induced stress by administration of adenosine, reveals areas with perfusion defects, which are directly caused by hemodynamically significant stenosis. Although this approach is promising as it merges the anatomic information of CCTA with functional analysis, it requires an additional CT scan which inevitably leads to higher radiation dose and longer examination time and the need for injection of pharmacological stress agents.
There is thus the need for a patient specific method to identify patients with functional significant stenosis in one or more coronary arteries based on the information extracted from a single CCTA dataset only, which has low computational complexity demands and which takes into account the status of the myocardial microvasculature and collateral flow, without relying on the detailed morphology of the coronary arterial system.